The ability to quickly and easily assemble hierarchically-arranged data and make calculations based upon such data (e.g., make predictions of future events) offers benefits in a wide variety of fields including medical research, strategic business planning, public transportation engineering, and many others. A number of data applications naturally lend themselves to hierarchical organization. For example, a geography may be hierarchically broken down from continent to country to state to city. As another example, a corporation may be hierarchically viewed from an entire company level, which contains a country level, which breaks down into members of a region level. The region level may then further decompose into additional levels.
FIG. 1 illustrates an example structure 30 for storing hierarchical data. A single root node 32 makes up the root level 34. The root node 32 contains two level 1 nodes 36, 38 that are related to the root node 32 by parent-child relationships 40, 42. Similarly, each of the level 1 nodes 36, 38 contains a plurality of level 2 child nodes 44. The number of levels and child nodes contained within a given parent node may vary to meet the needs of a given data application.